How do I make fully custom legends?

legends

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question: how do I (elegeantly) add fully custom legends in matplotlib?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "import epipy\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This function returns fig, ax object with two scatter plot objects and a fill_between object overlaid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def time_plot(df, yticks, date1, date2, color_col, color_dict=None):\n",
    "    \"\"\"\n",
    "    df = pandas dataframe of line list\n",
    "    yticks = column with ytick labels, e.g. case number\n",
    "    date1 = start date, e.g. onset date\n",
    "    date2 = end date, e.g. death date \n",
    "    color = column with color identifiers, e.g. patient sex or outcome\n",
    "    color_dict = optional dictionary with color categories as keys, and colors as values\n",
    "    ------------\n",
    "    returns fig, ax\n",
    "    ------------\n",
    "    Example useage:\n",
    "    fig, ax = time_plot(cases, 'case_id', 'onset_date', 'combined_outcome_date', 'categorical_outcome')\n",
    "    \"\"\"\n",
    "    fig, ax = plt.subplots()\n",
    "    ax.xaxis_date()\n",
    "    ax.set_aspect('auto')\n",
    "    fig.autofmt_xdate()\n",
    "    \n",
    "    plt.ylim(-1, len(df))\n",
    "    plt.yticks(np.arange(len(df)), df[yticks].values)\n",
    "    \n",
    "    color_keys = df[color_col].values\n",
    "    if color_dict == None:\n",
    "        color_choices = sns.color_palette('deep', len(color_keys))\n",
    "    color_dict = dict(zip(color_keys, color_choices))\n",
    "    \n",
    "    counter = 0\n",
    "    \n",
    "    for ix in df[date1].order(ascending=False).index:\n",
    "        x1 = df.xs(ix)[date1]\n",
    "        x2 = df.xs(ix)[date2]\n",
    "        \n",
    "        y1 = counter\n",
    "        \n",
    "        col = color_dict[df.xs(ix)[color_col]]\n",
    "        \n",
    "        ax.scatter(x1, y1, color=col, label='Test1')\n",
    "        ax.scatter(x2, y1, color=col, label='Test2')\n",
    "        plt.fill_between([x1, x2], y1-.04, y1+.04, alpha=.8, color=col)\n",
    "\n",
    "        counter += 1\n",
    "        \n",
    "    return fig, ax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generate example data using epipy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cases = epipy.generate_example_data(10, 30, 4, 5)\n",
    "cases2 = epipy.generate_example_data(10, 30, 4, 5)\n",
    "cases['Date2'] = cases2['Date']\n",
    "cases = cases.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Date</th>\n",
       "      <th>Cluster</th>\n",
       "      <th>sex</th>\n",
       "      <th>Date2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td> 0</td>\n",
       "      <td>2014-01-28</td>\n",
       "      <td> ClusterR</td>\n",
       "      <td>   Male</td>\n",
       "      <td>2014-01-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td> 1</td>\n",
       "      <td>2014-02-03</td>\n",
       "      <td> ClusterR</td>\n",
       "      <td> Female</td>\n",
       "      <td>2014-02-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td> 2</td>\n",
       "      <td>2014-02-03</td>\n",
       "      <td> ClusterR</td>\n",
       "      <td>   Male</td>\n",
       "      <td>2014-02-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td> 3</td>\n",
       "      <td>2014-02-09</td>\n",
       "      <td> ClusterR</td>\n",
       "      <td>   Male</td>\n",
       "      <td>2014-02-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td> 4</td>\n",
       "      <td>2014-02-09</td>\n",
       "      <td> ClusterR</td>\n",
       "      <td>   Male</td>\n",
       "      <td>2014-02-16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID       Date   Cluster     sex      Date2\n",
       "0   0 2014-01-28  ClusterR    Male 2014-01-26\n",
       "1   1 2014-02-03  ClusterR  Female 2014-02-03\n",
       "2   2 2014-02-03  ClusterR    Male 2014-02-05\n",
       "3   3 2014-02-09  ClusterR    Male 2014-02-09\n",
       "4   4 2014-02-09  ClusterR    Male 2014-02-16"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cases.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is what happens if I include a legend inline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x10b1101d0>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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tt0UR6QdcCqwGlgGnq+rKJNo8oDuOk2lidVsEJgK9VHW5iFyGecJcm0SbB3TH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\n4AHdcRwnI3hAdxzHyQj/HxqIl8LGNyDXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b110210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = time_plot(cases, 'ID', 'Date', 'Date2', 'sex')\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The solution I use instead is to make an invisible layer (note the alpha=0), and make a legend out of that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x10a5556d0>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fS4lumEOIxsW0JucbQbyfbyeOtl4GyG0/9kdLWuhmNh54P3AycWn0+LR9YMN9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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c1fc690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = time_plot(cases, 'ID', 'Date', 'Date2', 'sex', legend=False)\n",
    "color_dict = {'Male': 'b', 'Female':'r'}\n",
    "lines = []\n",
    "for key, value in color_dict.iteritems():\n",
    "    plt.scatter(cases['Date'][0], 1, color='b', alpha=0)\n",
    "    line = plt.Line2D(range(1), range(1), color=value, marker='o', markersize=6, alpha=.8, label=key)\n",
    "    lines.append(line)\n",
    "\n",
    "ax.legend(lines, [k for k in color_dict.iterkeys()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}